How do you calculate kurtosis examples?
x̅ is the mean and n is the sample size, as usual. m4 is called the fourth moment of the data set. m2 is the variance, the square of the standard deviation. The kurtosis can also be computed as a4 = the average value of z4, where z is the familiar z-score, z = (x−x̅)/σ.
How do you interpret kurtosis?
For kurtosis, the general guideline is that if the number is greater than +1, the distribution is too peaked. Likewise, a kurtosis of less than –1 indicates a distribution that is too flat. Distributions exhibiting skewness and/or kurtosis that exceed these guidelines are considered nonnormal.” (Hair et al., 2017, p.
How does Matlab calculate skewness?
Description. y = skewness( X ) returns the sample skewness of X . If X is a vector, then skewness(X) returns a scalar value that is the skewness of the elements in X . If X is a matrix, then skewness(X) returns a row vector containing the sample skewness of each column in X .
What is the value of kurtosis?
Kurtosis is a measure of the combined sizes of the two tails. It measures the amount of probability in the tails. The value is often compared to the kurtosis of the normal distribution, which is equal to 3.
What does a kurtosis of 3 mean?
A standard normal distribution has kurtosis of 3 and is recognized as mesokurtic. An increased kurtosis (>3) can be visualized as a thin “bell” with a high peak whereas a decreased kurtosis corresponds to a broadening of the peak and “thickening” of the tails. Kurtosis >3 is recognized as leptokurtic and <3.
How does MATLAB calculate kurtosis?
Description. k = kurtosis( X ) returns the sample kurtosis of X . If X is a vector, then kurtosis(X) returns a scalar value that is the kurtosis of the elements in X . If X is a matrix, then kurtosis(X) returns a row vector that contains the sample kurtosis of each column in X .